Data Integration
Build a unified view of your enterprise from existing corporate information.
Ensuring that your source data is consistent, accurate and reliable through a data quality process is the foundation for any successful business initiative. Yet no enterprise will have just one data source, and even after data quality has been enforced enterprisewide, the problem of integrating all of this data into a single, reliable platform remains. The data integration stage of the DataFlux methodology ensures that not only do you have good data, but you can also make good use of it.
DataFlux Methodology Stage 3 of 5 – Data Integration
Poor data integration can jeopardize ERP, CRM, data warehousing, business intelligence or any other initiative that relies on accurate data drawn from multiple sources. An effective data integration strategy can lower costs and improve productivity by ensuring the consistency, accuracy and reliability of data across your enterprise. Data integration enables you to:
- Match, link and consolidate multiple data sources together to create the best possible view of a customer, product, supplier, employee or asset
- Gain access to the right data sources at the right time to spur enhanced decision-making
- Ensure that high-quality information arrives at new data targets during data migration or consolidation efforts
- Access your data on any platform during an integration project
- Increase the quality of your business information before loading it into new systems
Understand Corporate Information Anywhere in the Enterprise
Data integration involves combining processes and technology to ensure your enterprise can make the most effective use of its data. Data integration can include:
| Data Movement |
Improve data quality when moving data from source to target:
- Extract data from data sources
- Correct inconsistencies
- Load the corrected data back into the source or into a new target
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| Data Linking and Matching |
Match information within or across data sources:
- Standardize formatting differences
- Identify significant information within multi-value fields
- Translate abbreviations or numeric codes
- Employ fuzzy matching rules
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| Data Householding |
Track customer purchases at the individual and household level:
- Track customer purchases at the individual and household level
- Reduce the cost of duplicate mailings
- Aggregate customer value by including all transactions or product details for other customers who live at the same address
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The Next Step: Data Enrichment
Integrated, high-quality data gives your organization the knowledge it needs to make sound, informed business decisions. But there's even more that can be accomplished, when you take your data beyond its own boundaries and incorporate a broader view of your customers and the world in which you do business through data enrichment >>